Abstract
Meteorological datasets with high-precision and high spatiotemporal resolution are an important base in many applications, such as climatology, ecology, and hydrology. To improve the spatial resolution and accuracy of meteorological data with different elements, this study proposes a method, whereby a machine learning (ML) algorithm is jointly applied to spatial downscaling and post-downscaling error correction (ML–ML). Taking a water conservation area of the upper Yellow River basin (UYRB) as a case study, and using the China Meteorological Forcing Dataset (CMFD), four ML algorithms (Gaussian process regression (GPR), neural network (NN), random forest (RF), and support vector machine (SVM)) were selected to verify the effectiveness of ML–ML and explore the optimal downscaling model suitable for different meteorological elements. The experimental results show the following: (1) the CMFD has good applicability in the UYRB; (2) in addition to the RF, the GRP, NN, and SVM models can successfully retain the original spatial distribution patterns of the CMFD dataset and reflect increased spatial detail; and (3) by comparing the performance of the four models in spatial downscaling and error correction of different meteorological elements, we find that the GPR model is best for precipitation, and the SVM model is best for relative humidity, 2-m air temperature, and 10-m wind speed. On the basis of the thinking behind the ML–ML method, the downscaling models applicable to different meteorological elements screened in this study can provide a reference for generating high-precision and high-resolution meteorological datasets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
References
Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Anderson M, Diak G, Gao F et al (2019) Impact of insolation data source on remote sensing retrievals of evapotranspiration over the California Delta. Remote Sens 11(3):216. https://doi.org/10.3390/rs11030216
Baez-Villanueva OM, Zambrano-Bigiarini M, Beck HE et al (2020) RF-MEP: a novel random forest method for merging gridded precipitation products and ground-based measurements. Remote Sens Environ 239:111606. https://doi.org/10.1016/j.rse.2019.111606
Bakure BZ, Hundera K, Abara M (2022) Review on the effect of climate change on ecosystem services. In IOP Conference Series: Earth and Environmental Science (Vol. 1016, No. 1, 012055 IOP Publishing. https://doi.org/10.1088/1755-1315/1016/1/012055
Bell B, Hersbach H, Simmons A et al (2021) The ERA5 global reanalysis: preliminary extension to 1950. Q J R Meteorol Soc 147(741):4186–4227. https://doi.org/10.1002/qj.4174
Bong T, Son YH, Yoo SH, Hwang SW (2018) Nonparametric quantile mapping using the response surface method–bias correction of daily precipitation. Journal of Water and Climate. Change 9(3):525–539. https://doi.org/10.2166/wcc.2017.127
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Brümmer C, Black TA, Jassal RS et al (2012) How climate and vegetation type influence evapotranspiration and water use efficiency in Canadian forest, peatland and grassland ecosystems. Agric Forest Meteorol 153:14–30. https://doi.org/10.1016/j.agrformet.2011.04.008
Chang TJ (1991) Investigation of precipitation droughts by use of Kriging method. J Irrig Drain Eng 117(6):935–943. https://doi.org/10.1061/(ASCE)0733-9437(1991)117:6(935)
Cheema MJM, Bastiaanssen WG (2012) Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus basin. Int J Remote Sens 33(8):2603–2627. https://doi.org/10.1080/01431161.2011.617397
Cosgrove BA, Dag L, Mitchell KE et al (2003) Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J Geophys Res: Atmos 108(D22). https://doi.org/10.1029/2002JD003118
Cutler A, Cutler DR, Stevens JR (2012) Random forests. In: Ensemble machine learning. Springer, Boston, MA, pp 157–175
Dangol S, Talchabhadel R, Pandey VP (2022) Performance evaluation and bias correction of gridded precipitation products over Arun River Basin in Nepal for hydrological applications. Theor Appl Climatol 148(3):1353–1372. https://doi.org/10.1007/s00704-022-04001-y
Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597. https://doi.org/10.1002/qj.828
Dhakal S, Sedhain GK, Dhakal SC (2016) Climate change impact and adaptation practices in agriculture: a case study of Rautahat District. Nepal Clim 4(4):63. https://doi.org/10.3390/cli4040063
Duan Z, Bastiaanssen WGM (2013) First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure. Remote Sens Environ 131:1–13. https://doi.org/10.1016/j.rse.2012.12.002
Ebita A, Kobayashi S, Ota Y et al (2011) The Japanese 55-year reanalysis “JRA-55”: an interim report. Sola 7:149–152. https://doi.org/10.2151/sola.2011-038
Ghorbani MA, Shamshirband S, Haghi DZ, Azani A, Bonakdari H, Ebtehaj I (2017) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res 72:32–38. https://doi.org/10.1016/j.still.2017.04.009
Ghorbanpour AK, Hessels T, Moghim S, Afshar A (2021) Comparison and assessment of spatial downscaling methods for enhancing the accuracy of satellite-based precipitation over Lake Urmia Basin. J Hydrol 596:126055. https://doi.org/10.1016/j.jhydrol.2021.126055
He J, Yang K, Tang WJ, Lu H, Qin J, Chen YY, Li X (2020) The first high-resolution meteorological forcing dataset for land process studies over China. Scientific Data 7(1):1–11. https://doi.org/10.1038/s41597-020-0369-y
Hellström C, Chen DL (2003) Statistical downscaling based on dynamically downscaled predictors: application to monthly precipitation in Sweden. Adv Atmos Sci 20(6):951–958. https://doi.org/10.1007/BF02915518
Hutengs C, Vohland M (2016) Downscaling land surface temperatures at regional scales with random forest regression. Remote Sens Environ 178:127–141. https://doi.org/10.1016/j.rse.2016.03.006
Jaberalansar Z, Tarkesh M, Bassiri M (2018) Spatial downscaling of climate variables using three statistical methods in Central Iran. J Mt Sci 15(3):606–617. https://doi.org/10.1007/s11629-016-4289-4
Khalili M, Van Nguyen VT (2017) An efficient statistical approach to multi-site downscaling of daily precipitation series in the context of climate change. Clim Dyn 49(7-8):2261–2278. https://doi.org/10.1007/s00382-016-3443-6
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980
Kour R, Patel N, Krishna AP (2016) Climate and hydrological models to assess the impact of climate change on hydrological regime: a review. Arab J Geosci 9:1–31. https://doi.org/10.1007/s12517-016-2561-0
Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529:84–87. https://doi.org/10.1038/nature16467
Li L (2019) Geographically weighted machine learning and downscaling for high-resolution spatiotemporal estimations of wind speed. Remote Sens 11(11):1378. https://doi.org/10.3390/rs11111378
Li X, Cheng GC, Lu L (2005) Spatial analysis of air temperature in the Qinghai-Tibet Plateau. Arct Antarct Alp Res 37(2):246–252. https://doi.org/10.1657/1523-0430(2005)037[0246:SAOATI]2.0.CO;2
Li X, Li Z, Huang W, Zhou P (2020) Performance of statistical and machine learning ensembles for daily temperature downscaling. Theor Appl Climatol 140:571–588. https://doi.org/10.1007/s00704-020-03098-3
Lima CH, Kwon HH, Kim YT (2021) A Bayesian Kriging model applied for spatial downscaling of daily rainfall from GCMs. J Hydrol 597:126095. https://doi.org/10.1016/j.jhydrol.2021.126095
Mackay DS, Ewers BE, Cook BD, Davis KJ (2007) Environmental drivers of evapotranspiration in a shrub wetland and an upland forest in northern Wisconsin. Water Resour Res 43(3). https://doi.org/10.1029/2006WR005149
Min XX, Ma ZQ, Xu JT, He K, Wang ZG, Huang QL, Li J (2020) Spatially downscaling IMERG at daily scale using machine learning approaches over Zhejiang, southeastern China. Front Earth Sci 8:146. https://doi.org/10.3389/feart.2020.00146
Price DT, McKenney DW, Nalderet IA, Hutchinson MF, Kestevend JL (2000) A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric Forest Meteorol 101(2–3):81–94. https://doi.org/10.1016/S0168-1923(99)00169-0
Salcedo-Sanz S, Deo RC, Carro-Calvo L, Saavedra-Moreno B (2016) Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theor Appl Climatol 125(1):13–25. https://doi.org/10.1007/s00704-015-1480-4
Seeger M (2004) Gaussian processes for machine learning. Int J Neural Syst 14(2):69–106. https://doi.org/10.1142/S0129065704001899
Solano JC, Montaño T, Maldonado-Correa J, Ordóñez A, Pesantez M (2021) Correlation between the wind speed and the elevation to evaluate the wind potential in the southern region of Ecuador. Energy Rep 7:259–268. https://doi.org/10.1016/j.egyr.2021.06.044
Sun S, Shi CX, Pan Y, Pan Y, Bai L, Xu B, Zhang T, Han S, Jiang LP (2020) Applicability assessment of the 1998–2018 CLDAS multi-source precipitation fusion dataset over China. J Meteorol Res 34(4):879–892. https://doi.org/10.1007/s13351-020-9101-2
Wang SC (2003) Artificial neural network. In: Interdisciplinary computing in java programming. Springer, Boston, MA, pp 81–100
Whitehead PG, Wilby RL, Battarbee RW, Kernan M, Wade AJ (2009) A review of the potential impacts of climate change on surface water quality. Hydrol Sci J 54(1):101–123. https://doi.org/10.1623/hysj.54.1.101
Wu YC, Zhang ZH, Crabbe MJC, Chandra Das L (2022) Statistical learning-based spatial downscaling models for precipitation distribution. Adv Meteorol 2022. https://doi.org/10.1155/2022/3140872
Xiao MZ, Zhang Q, Singh VP, Chen XH (2013) Regionalization-based spatiotemporal variations of precipitation regimes across China. Theor Appl Climatol 114(1):203–212. https://doi.org/10.1007/s00704-013-0832-1
Xu SP, Zhao QJ, Yin K, He GJ, Zhang ZM, Wang GZ, Wen MP, Zhang N (2021a) Spatial downscaling of land surface temperature based on a multi-factor geographically weighted machine learning model. Remote Sens 13(6):1186. https://doi.org/10.3390/rs13061186
Xu ZF, Han Y, Tam CY, Yang ZL, Fu C (2021b) Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100). Sci Data 8(1):1–11. https://doi.org/10.1038/s41597-021-01079-3
Yan X, Chen H, Tian BR, Sheng S, Wang JX, Kim JS (2021) A downscaling-merging scheme for improving daily spatial precipitation estimates based on random forest and cokriging. Remote Sens 13(11):2040. https://doi.org/10.3390/rs13112040
Yang J, Xie BP, Zhang DG, Tao WQ (2021) Climate and land use change impacts on water yield ecosystem service in the Yellow River basin, China. Environ Earth Sci 80(3):1–12. https://doi.org/10.1007/s12665-020-09277-9
Yang K, He J (2019) China meteorological forcing dataset (1979-2018). National Tibetan Plateau Data Center. https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file
Yang Y, LuoY (2014) Using the back propagation neural network approach to bias correct TMPA data in the arid region of Northwest China. J Hydrometeorol 15(1):459–473. https://doi.org/10.1175/JHM-D-13-041.1
Zhang L, Li X, Zheng DH, Zhang K, Ma QM, Zhao YB, Ge YC (2021a) Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach. J Hydrol 594:125969. https://doi.org/10.1016/j.jhydrol.2021.125969
Zhang YC, Du JQ, Guo L, Sheng ZL, Wu JH, Zhang J (2021b) Water conservation estimation based on time series NDVI in the Yellow River basin. Remote Sens 13(6):1105. https://doi.org/10.3390/rs13061105
Zhang YY, Shen XJ, Fan GH (2021c) Elevation-dependent trend in diurnal temperature range in the Northeast China during 1961–2015. Atmosphere 12(3):319. https://doi.org/10.3390/atmos12030319
Zhang ZD, Ye L, Qin H, Liu YQ, Wang C, Yu X, Yin XL, Li J (2019) Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression. Appl Energy 247:270–284. https://doi.org/10.1016/j.apenergy.2019.04.047
Zhu DY, Yang Q, Xiong KN, Xiao H (2022) Spatiotemporal variations in daytime and night-time precipitation on the Yunnan-Guizhou Plateau from 1960 to 2017. Atmosphere 13(3):415. https://doi.org/10.3390/atmos13030415
Code availability
The codes are available from the corresponding author on reasonable request.
Funding
This work was supported by the National Natural Science Foundation of China, grant number 42171274.
Author information
Authors and Affiliations
Contributions
Ying Cao, methodology, validation, software, writing—original draft, and writing—review and editing. Biao Zeng, conceptualization, resources, supervision, and writing—review and editing. Fuguang Zhang, resources, investigation, and writing—review and editing. Yanqi Shen, resources, validation, and writing—review and editing. Zhenhua Meng, investigation and writing—review and editing. Yong Jiang, investigation and writing—review and editing. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval
Not applicable
Consent to participate
All authors have consented to participate in this process.
Consent for publication
All authors have agreed to publish this paper.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Cao, Y., Zeng, B., Zhang, F. et al. A spatial downscaling method for multielement meteorological data: case study from a water conservation area of the upper Yellow River basin. Theor Appl Climatol 153, 853–871 (2023). https://doi.org/10.1007/s00704-023-04505-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-023-04505-1